C86-1145 finding the ` uniqueness point ' for word recognition . Let us assume ( after Marslen
C86-1140 utterances requires more than mere word recognition . It is based on a number of
C82-1006 measured the goodness of each word recognition . To this purpose a method to
C02-2002 effect of lexicalization on new word recognition . We parsed all the 121,863 sentences
C86-1145 the widespread Cohort Model of word recognition . The parser implicit in the
C86-1140 variabilities and vagueness of the word recognition process , semantics in a speech
C80-1070 connected words . But in either case , word recognition has been thought a basic problem
C86-1145 for the next hypothesis as to word recognition . This is actually enough as
A94-1033 Table 1 . The topl correct rate of word recognition is as low as 57 % . Relaxation
A94-1031 Table 1 . The topl correct rate of word recognition is as low as 57 % . Relaxation
C00-2169 Hem-nan , 1997 ) . He redefines the word recognition problem to identify the best
C86-1140 a satisfying certainty by the word recognition module . This is a very hard
A94-1033 a useful tool to post-process word recognition results ( -LSB- l , 4 -RSB- )
A94-1031 a useful tool to post-process word recognition results ( -LSB- l , 4 -RSB- )
acl-2001-inv1 with small vocabulary isolated word recognition and with speaker-dependent (
C86-1140 mentioned verb ) as also for the word recognition of the system . The special problem
C02-1148 segmenters that achieve 90 % and 95 % word recognition accuracy respectively . Finally
A92-1016 and surface representations . Word recognition is reduced to finding valid lexical
C86-1140 structures , and to guide the word recognition process by expectations resulting
acl-2001-inv1 acoustic and language models . Word recognition is carried out in one or more
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